The confounding effects of cell aggregates and cell debris on cell cycle analysis of DNA histograms obtained by flow cytometry (FCM) are well recognized (Bagwell C. Theoretical aspects of flow cytometry data analysis. In: Bauer K, Duque R, Shankey T, editors. Clinical Flow Cytometry. Baltimore: Williams and Wilkins; 1993. p. 41-61; Rabinovitch P. Practical Considerations for DNA content and cell cycle analysis. In: Bauer K, Duque R, Shankey T, editors. Clinical Flow Cytometry. Baltimore: Williams and Wilkins 117-142; 1993; Shankey TV, Rabinovitch PS, Bagwell B, Bauer KD, Duque RE, Hedley DW, et al. Guidelines for implementation of clinical DNA cytometry. International Society for Analytical Cytology. Cytometry 1993;14(5):472-7; Rabinovitch PS. DNA content histogram and cell-cycle analysis. Methods Cell Biol 1994;41:263-96; Heiden T, Castro J, Graf BM, Tribukait B. Comparison of routine flow cytometric DNA analysis of fresh tissues in two laboratories: effects of differences in preparation methods and background models of cell cycle calculation. Cytometry 1998;34(4):187-97; Wersto RP, Chrest FJ, Leary JF, Morris C, Stetler-Stevenson MA, Gabrielson E. Doublet discrimination in DNA cell-cycle analysis. Cytometry 2001;46 (5):296-306, all of which are incorporated by reference herein). Various approaches have been adopted for dealing with the problem of cell aggregates. Signal pulse shape characteristics can be used to identify cell aggregates at the time of measurement, in order to exclude them from the initial list mode data file. In practice, this approach leaves much to be desired in the analysis of disaggregated cell suspensions obtained from human solid tumors (Rabinovitch P. Practical Considerations for DNA content and cell cycle analysis. In: Bauer K, Duque R, Shankey T, editors. Clinical Flow Cytometry. Baltimore: Williams and Wilkins 117-142; 1993; Wersto RP, Chrest FJ, Leary JF, Morris C, Stetler-Stevenson MA, Gabrielson E. Doublet discrimination in DNA cell-cycle analysis. Cytometry 2001;46(5):296-306, both of which are incorporated by reference herein). Mathematical models have been developed to deal with binned DNA histogram data to estimate the contribution of cell aggregates to various cell cycle phase regions (Bagwell C. Theoretical aspects of flow cytometry data analysis. In: Bauer K, Duque R, Shankey T, editors. Clinical Flow Cytometry. Baltimore: Williams and Wilkins; 1993. p. 41-61; Rabinovitch P. Practical Considerations for DNA content and cell cycle analysis. In: Bauer K, Duque R, Shankey T, editors. Clinical Flow Cytometry. Baltimore: Williams and Wilkins 117-142; 1993, both of which are incorporated by reference herein). However, such models do not address the effects of cell aggregation on correlated multiparameter non-DNA measurements performed on the same cells. Previously, a simple statistical approach to excluding cell aggregates from bivariate DNA/Her-2/neu data (Shackney SE, Pollice AA, Smith CA, Alston L, Singh SG, Janocko LE, et al. The Accumulation of Multiple Genetic Abnormalities in Individual Tumor Cells in Human Breast Cancers: Clinical Prognostic Implications. Cancer J Sci Am 1996;2 (2):106, incorporated by reference herein) was developed, but the method is cumbersome, and it may be of limited applicability.
Clinical multiparameter FCM studies in human breast cancer have shown that quantitative measurements of cell DNA content, Her-2/neu levels, and ras protein levels in the same cells are of clinical prognostic significance (Shackney SE, Pollice AA, Smith CA, Alston L, Singh SG, Janocko LE, et al. The Accumulation of Multiple Genetic Abnormalities in Individual Tumor Cells in Human Breast Cancers: Clinical Prognostic Implications. Cancer J Sci Am 1996;2 (2):106; Shackney S, Smith C, Pollice A, Brown K, Day R, Julian T, et al. Intracellular patterns of Her-2/neu, ras, and ploidy abnormalities in primary human breast cancers predict clinical disease free survival. In: Annual Meeting of the United States and Canadian Academy of Pathology; 2003; Washington, D.C.; 2003. p. 46A, both of which are incorporated by reference herein). Better methods were needed for development for the identification of cell aggregates in cell suspensions obtained from human solid tumors, in order to improve the ability to extract clinical prognostic information from quantitative interrelationships among multiple constituents within in each cell in such samples.
Laser scanning cytometry (LSC) is a technology that provides the opportunity to correlate multiple measurements performed on individual cells with their morphologic appearance on a cell by cell basis. The present invention involves an approach to cell aggregate discrimination that relies on features that are readily measured in each cell by LSC, in order to identify cell aggregates and exclude them from the list mode data file. This approach is validated by direct observation of ˜400 individual cells in each of 21 samples of normal and malignant human cells from a variety of sources. It has been found that this approach reduces the proportions of cell aggregates in clinical samples from a mean of 20% (range, 6% to 56%) to a mean of 2.4% (range, 0-7%).